AWS CloudFormation Validate

Validate AWS CloudFormation templates from Python and catch schema violations, semantic errors, security risks, and best-practice findings before deployment - in your editor, build, service, or CI.

• Offline - all rules and CloudFormation resource schemas are bundled; nothing is fetched at runtime and no AWS credentials are needed.
• Fast - engines and schemas compile once and are reused across validations; typical templates validate in under a second.
• Self-contained - each platform wheel bundles its matching native library.

All types are importable from the top-level cloudformation_validate package.

Installation

Available on PyPI as cloudformation-validate.

pip install cloudformation-validate

Requires Python 3.9 or later. The package has no runtime dependencies. PyPI publishes a separate wheel for every supported native target; each wheel carries exactly one native library and an accurate platform tag, so pip downloads only the artifact compatible with the installing host.

Quick start

from cloudformation_validate import CompositeEngine

engine = CompositeEngine()
report = engine.validate_template("template.yaml")
for d in report.diagnostics:
    print(f"[{d.severity.name}] {d.rule_id}: {d.message}")

Each diagnostic identifies the rule, severity, affected entity and property, and source location - see Diagnostic.

Engines are expensive to construct (rules compile once) and cheap to reuse - create one engine and validate many templates. Every fallible call raises ValidationError on failure; internal panics are caught at the FFI boundary and surface as the same exception, never a process abort. version() returns the version of the bundled validation core.

A template is passed as a Template: a file path (str or os.PathLike, read from disk; the path is used for diagnostic source locations), raw bytes, or a TemplateContent carrying str text or bytes already in memory together with an optional name that labels the report and its diagnostics exactly like a file path does. An in-memory template with no name is labelled "template" (DEFAULT_TEMPLATE_NAME).

Template = str | os.PathLike | bytes | TemplateContent

class TemplateContent:
    def __init__(self, content: str | bytes, name: str = DEFAULT_TEMPLATE_NAME): ...

report = engine.validate_template(b"Resources: {}")
report = engine.validate_template(TemplateContent("Resources: {}", "inline.yaml"))

TemplateModel and SchemaValidator.validate accept the same Template forms.

Engine

RegoEngine and CelEngine both subclass Engine and are interchangeable - they produce identical diagnostics for the same template and config. CompositeEngine also subclasses Engine and layers custom Rego, CEL, and Guard rules on top of the built-in rules - see CompositeEngine.

Engine base class

class Engine:
    def validate_template(self, template: Template, config: ValidateConfig | None = None) -> ValidationReport: ...
    def validate_aws_cli_command(self, request: AwsCliCommand) -> AwsCliCommandValidation: ...
    def list_rules(self) -> list[RuleInfo]: ...
    def engine_name(self) -> str: ...

EngineConfig

Passed to the constructor. All fields are optional: the rule lists default to empty and a None schema_validator_config uses only the bundled schemas.

@dataclass
class EngineConfig:
    custom_rules: list[ExternalRuleSource] = []                 # engine-native rules (Rego for RegoEngine, CEL for CelEngine)
    guard_rules: list[ExternalRuleSource] = []                  # CloudFormation Guard DSL rules - evaluated by the Guard evaluator
    schema_validator_config: SchemaValidatorConfig | None = None  # additional resource provider schemas

@dataclass
class SchemaValidatorConfig:
    additional_schemas: list[AdditionalSchemaSource] = []  # resource provider schemas merged over the bundled schemas

@dataclass
class ExternalRuleSource:
    name: str     # identifier shown in diagnostics (e.g. file path)
    content: str  # full rule source text

@dataclass
class AdditionalSchemaSource:
    type_name: str | None = None  # None to use the typeName inside the schema JSON
    schema: str                   # complete resource provider schema JSON

def file_to_external_rule_source(path) -> ExternalRuleSource: ...                       # rule file read from disk; the path becomes the rule source name
def file_to_additional_schema_source(path, type_name=None) -> AdditionalSchemaSource: ...  # schema file; type_name defaults to the value inside the JSON

Each rule is an ExternalRuleSource - name identifies the rule in diagnostics and content is the full rule source text. Use file_to_external_rule_source(path) to load one from disk (the same pattern as passing a template path to validate_template), or construct an ExternalRuleSource(name, content) when you already have the rule text in memory. Each additional schema is an AdditionalSchemaSource - a complete resource provider schema JSON plus an optional type_name that may be omitted when the schema JSON contains its own typeName; file_to_additional_schema_source(path) loads one from disk. Additional schemas extend the bundled schemas or register resource types CloudFormation has not published yet; a malformed, contradictory, or unsupported schema fails engine construction rather than silently weakening validation. Guard rules are evaluated by the CloudFormation Guard evaluator itself against the template as written, so every engine reports exactly what cfn-guard validate reports; a Guard file that does not parse also fails engine construction. The two forms can be mixed freely:

from cloudformation_validate import (
    CelEngine, EngineConfig, SchemaValidatorConfig, file_to_additional_schema_source, file_to_external_rule_source,
)

engine = CelEngine(
    EngineConfig(
        custom_rules=[file_to_external_rule_source("rules/s3_encryption.json")],
        guard_rules=[file_to_external_rule_source("rules/compliance.guard")],
        schema_validator_config=SchemaValidatorConfig(
            additional_schemas=[file_to_additional_schema_source("schemas/aws-lambda-function.json")],
        ),
    ),
)

See Custom Rules for the Rego, CEL, and Guard rule formats and Additional Resource Provider Schemas for the schema merge model.

CompositeEngine

CompositeEngine also subclasses Engine but takes a CompositeEngineConfig. It evaluates every built-in rule with a fixed built-in CEL evaluator and layers the caller-supplied custom rules on top: custom CEL and Guard rules run alongside that built-in engine, while custom Rego rules run in a separate external engine that is constructed only when Rego rules are supplied. With no custom rules it produces the same built-in diagnostics as RegoEngine and CelEngine, and engine_name() returns "composite". Because the composite fixes which engine owns the built-ins, the config has no custom_rules field - it carries only the custom rules layered on top:

@dataclass
class CompositeEngineConfig:
    rego_rules: list[ExternalRuleSource] = []                     # custom Rego rules, run by the external engine
    cel_rules: list[ExternalRuleSource] = []                      # custom CEL rules, run by the built-in engine
    guard_rules: list[ExternalRuleSource] = []                    # CloudFormation Guard DSL rules, evaluated alongside the built-in engine
    schema_validator_config: SchemaValidatorConfig | None = None  # additional resource provider schemas, observed by both inner engines

from cloudformation_validate import CompositeEngine, CompositeEngineConfig, file_to_external_rule_source

engine = CompositeEngine(
    CompositeEngineConfig(
        rego_rules=[file_to_external_rule_source("rules/s3_naming.rego")],
        guard_rules=[file_to_external_rule_source("rules/compliance.guard")],
    ),
)
report = engine.validate_template("template.yaml")

ValidateConfig

Controls filtering, detail, severity, parameter overrides, and behavior for one validation call. All fields have defaults - omitting the config or passing ValidateConfig() uses them.

from cloudformation_validate import RuleFilterConfig, Severity, ValidateConfig

report = engine.validate_template(
    "template.yaml",
    ValidateConfig(
        exclude=RuleFilterConfig(ids=["I1002"]),
        severity_level=Severity.WARN,
    ),
)

@dataclass
class ValidateConfig:
    include: RuleFilterConfig = RuleFilterConfig()
    exclude: RuleFilterConfig = RuleFilterConfig()
    detail_level: DetailLevel | None = None       # None = DETAILED
    severity_level: Severity | None = None        # None = INFO
    parameter_overrides: dict[str, str] = {}
    pseudo_parameter_overrides: PseudoParameterOverrides = PseudoParameterOverrides()
    strict: bool | None = None                    # None = False
    disable_builtin_rules: bool | None = None     # None = False

RuleFilterConfig

Both include and exclude use this structure. All fields are additive - a rule matches if it hits any criterion.

@dataclass
class RuleFilterConfig:
    ids: list[str] = []                             # exact rule IDs, e.g. ["E3012", "W3010"]
    categories: list[str] = []                      # category names, e.g. ["security", "best_practices"]
    id_ranges: list[IdRange] = []                   # numeric ranges, e.g. IdRange(prefix="E", start=3000, end=3099)
    id_patterns: list[str] = []                     # regex patterns matched against rule IDs
    resource_ids: list[ResourceIdFilter] = []       # a rule (or every rule) on a logical resource ID
    logical_ids: list[LogicalIdFilter] = []         # a rule (or every rule) on a named template entity
    resource_types: list[ResourceTypeFilter] = []   # a rule (or every rule) on a resource type
    services: list[ServiceFilter] = []              # a rule (or every rule) on a service, e.g. "AWS::AutoScaling"

# resource_ids / logical_ids / resource_types / services each carry an optional rule_id:
# set it to scope the filter to one rule, or leave it None for every rule on the target.
@dataclass
class ResourceIdFilter:   rule_id: str | None = None; resource_id: str
@dataclass
class LogicalIdFilter:    rule_id: str | None = None; logical_id: str; entity_type: EntityType | None = None
@dataclass
class ResourceTypeFilter: rule_id: str | None = None; resource_type: str
@dataclass
class ServiceFilter:      rule_id: str | None = None; service: str

The service is matched verbatim against the service-provider::service-name prefix of the resource type - its first two ::-delimited segments (e.g. AWS::AutoScaling in AWS::AutoScaling::LaunchConfiguration).

The resource_ids dimension matches only diagnostics attributed to a resource; logical_ids additionally matches diagnostics on parameters, outputs, mappings, conditions, and template rules (for resource diagnostics the two carry the same value). A non-None entity_type scopes a LogicalIdFilter to entities of one type, so MyThing as a PARAMETER is matched without touching a same-named entity of another type.

PseudoParameterOverrides

Override CloudFormation pseudo-parameters used during intrinsic function resolution. All fields are optional - when None, the engine uses built-in defaults (e.g. region defaults to us-east-1).

@dataclass
class PseudoParameterOverrides:
    account_id: str | None = None         # AWS::AccountId
    notification_arns: str | None = None  # AWS::NotificationARNs
    partition: str | None = None          # AWS::Partition
    region: str | None = None             # AWS::Region (default: "us-east-1")
    stack_id: str | None = None           # AWS::StackId
    stack_name: str | None = None         # AWS::StackName
    url_suffix: str | None = None         # AWS::URLSuffix

TemplateModel

Parses a template into the resolved SemanticModel for direct inspection - the same model the engines evaluate rules against.

model = TemplateModel("template.yaml")  # a path, bytes, or TemplateContent, like the engines
in_memory = TemplateModel(TemplateContent("Resources: {}"))

SchemaValidator

Runs schema validation independently from the rule engines. Checks each resource against the compiled CloudFormation provider schemas and produces FATAL-severity diagnostics for structural violations. The optional constructor argument is the same SchemaValidatorConfig accepted by EngineConfig; omitting it uses only the bundled schemas.

validator = SchemaValidator()
diagnostics = validator.validate("template.yaml")

AWS CLI command validation

validate_aws_cli_command models an AWS CLI (or SDK) API call as CloudFormation resource state and validates it offline before it is sent. It classifies the operation, maps it to a CloudFormation resource type through a closed, generated adapter catalog, synthesizes a template from the supplied parameters, and runs the normal template pipeline on it. A TemplateBody parameter of a CloudFormation operation is validated as-is. Any request that cannot be modeled exactly - an unregistered operation, a parameter without a lossless property mapping, or a value outside a CloudFormation constraint the API itself does not enforce - is skipped with a reason, never guessed.

from cloudformation_validate import AwsCliCommand, AwsCliCommandValidationStatus, CompositeEngine

engine = CompositeEngine()
request = AwsCliCommand("s3", "CreateBucket", {"Bucket": "example-bucket"})
validation = engine.validate_aws_cli_command(request)
if validation.status == AwsCliCommandValidationStatus.VALIDATED:
    for d in validation.report.diagnostics:
        print(f"[{d.severity.name}] {d.rule_id}: {d.message}")
else:
    print(f"skipped ({validation.operation_kind.name}): {validation.reason}")

class AwsCliCommand:
    def __init__(
        self,
        service_name: str,                 # canonical botocore service name, e.g. "s3" or "cloudformation"
        operation_name: str,               # API operation name, e.g. "CreateBucket"
        parameters: Mapping[str, object],  # request parameters
        *,
        service_prefix: str | None = None, # signing prefix; context only
        http_method: str | None = None,    # classification hint ("GET"/"HEAD"/"DELETE") for unrecognized verbs
        is_read_only: bool | None = None,  # True classifies the operation as READ_ONLY
    ): ...

• service_name is matched case-insensitively. Signing names, endpoint aliases, and ARN prefixes are never resolved; translate an SDK's service identity first.
• parameters accepts nested mappings and sequences, str, int, float, bool, None, bytes, and datetime.datetime (serialized as ISO 8601) - the same values used by botocore request dictionaries. Any other value is carried as an explicit unsupported marker, and because synthesis is all-or-nothing the request is then skipped with a reason naming the offending parameter - no parameter is ever silently dropped.

The result is an AwsCliCommandValidation:

The full contract - the adapter catalog, all-or-nothing mapping, and which rules are dropped for synthesized state - is documented in validation-engine/API.md.

Report Types

ValidationReport

validate_template always returns a ValidationReport - a template syntax failure is returned as a report with ReportStatus.ERROR and an F1101 diagnostic; only infrastructure or engine failures raise:

@dataclass
class ValidationReport:
    file_path: str
    status: ReportStatus  # OK, ANALYSIS_INCOMPLETE (findings may be omitted), or ERROR (pipeline failure)
    version: str
    metadata: ReportMetadata
    performance: PerformanceMetrics
    diagnostics: list[Diagnostic]

Every finding is a Diagnostic (see Diagnostic). Its enrichment fields - documentation_url, rule_description, phase (PARSE | SCHEMA | LINT), and context (ViolationContext with actual_value, expected_constraint, resolution_source, etc.) - are populated only at detail_level DETAILED (the default); validating at STANDARD leaves them None, keeping the base diagnostic fields.

metadata carries the summary counts, the number of suppressed diagnostics, the resources scanned and rules evaluated, the strict flag and severity threshold used, and optional budget-exhaustion records. Each budget-exhaustion record retains a stable machine-readable kind and also includes a human-readable description sentence, the numeric limit, and whether that specific exhaustion makes analysis incomplete. requiredPropertyCombinations is context-only, so its analysis_incomplete value is False and the report can remain ReportStatus.OK.

Diagnostic

@dataclass
class Diagnostic:
    rule_id: str                        # e.g. "E3012", "F1001", "W3010"
    severity: Severity                  # FATAL, ERROR, WARN, INFO, DEBUG
    message: str
    source: RuleOrigin                  # SCHEMA, CFN_LINT, ENGINE, CUSTOM, GUARD
    entity: Entity | None               # the named template entity the finding targets, if any
    property_path: str | None           # e.g. "Properties.BucketName", or section-absolute like "Parameters/MyParam/Type"
    suggested_fix: str | None
    category: str | None
    start_line: int | None
    start_column: int | None
    end_line: int | None
    end_column: int | None
    related_resources: list[RelatedResource] | None
    condition_scenario: dict[str, bool] | None  # condition truth assignment that triggers this diagnostic
    # Enrichment fields: populated at detail_level DETAILED (the default), None at STANDARD.
    documentation_url: str | None
    rule_description: str | None
    phase: Phase | None                 # PARSE | SCHEMA | LINT - pipeline stage that produced the finding
    context: ViolationContext | None    # actual_value, expected_constraint, resolution_source, etc.

# The named template entity a diagnostic is attributed to. The entity type is the
# singular form of the top-level template section the entity is declared in.
@dataclass
class Entity:
    logical_id: str                     # logical ID as declared in the template
    entity_type: EntityType
    resource_type: str | None = None    # CloudFormation type, when the entity is a resource whose type is known

class EntityType(enum.Enum):
    RESOURCE, PARAMETER, OUTPUT, MAPPING, METADATA, RULE, CONDITION, TRANSFORM, FORMAT_VERSION, DESCRIPTION

Severity, RuleOrigin, DetailLevel, and ReportStatus are enum.Enum classes; use .name for the string form (Severity.WARN.name == "WARN").